Remove or replace trees from a TreeBagger ensemble
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Hello,
There is this method TreeBagger.combine that can combine two independently trained TreeBagger classifiers.
The problem is that I have to combine two ensembles, but I want to replace randomly 25% of the trees from the first ensemble with trees from the second ensemble.
Three possible solutions to overcome my problem:
1. Method to remove specified trees from ensemble. Then I would just remove 25% of the ensemble with random indexes and combine with the second ensemble.
2. Method to construct new TreeBagger from a collection of trees. For example something like this:
TreeBagger=Construct(Tree1, Tree2, ...);
3. Method to replace trees in TreeBagger ensemble. For example I tried to do this, like in a structure:
ensemble.Trees{i}=new_tree;
but that didn't work, because the property is private.
Please help me, because I had no progress with any of three...
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Ilya
2012-3-9
Combining two objects would be hard. You can work around this by growing one big ensemble and treating parts of it as separate ensembles. Take a look at the 'trees' argument for PREDICT and similar mehods.
This recipe is justified because every tree in a TreeBagger ensemble is grown independently of others. For prediction, trees are combined by simple averaging. This approach would not work for ensembles of other types.
Here is how I effectively grow two TreeBagger ensembles and then combine them:
% Load data
load ionosphere;
% Grow two ensembles, 100 trees each
b = TreeBagger(200,X,Y,'oobpred','on');
% Out-of-bag prediction from 1st ensemble
Yoob1 = oobPredict(b,'trees',1:100);
% Out-of-bag prediction from 2nd ensemble
Yoob2 = oobPredict(b,'trees',101:200);
% Randomly drop 25 trees from the 1st ensemble and combine the two
% ensembles.
idx1 = datasample(1:100,75,'replace',false);
Yoob3 = oobPredict(b,'trees',[idx1 101:200]);
更多回答（1 个）
Artik Crazy
2012-3-10
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Ilya
2012-3-10
Various properties of your PrunedEnsemble are now broken. For example, OOBIndices are the indices of observations that are out-of-bag for the grown trees. Tree 101 in OriginalEnsemble must use column 101 of OOBIndices. In PrunedEnsemble, this tree has index 1 and will use column 1 of OOBIndices instead. Because the tree list and OOBIndices no longer match, you cannot use oobPredict anymore. This is just one example; there are other broken properties.
If you must go down this road, I would recommend modifying CompactTreeBagger in a similar fashion. You should then execute COMPACT on a TreeBagger object before removing trees. The compact class is simpler, and there is less chance you will screw something up by these changes. Certainly, the PREDICT method will work fine, but I cannot vouch for everything else.
Artik Crazy
2012-3-10
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2012-3-9
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